Regression model applied to a two-stage charger rock in a surface mining operation

• Autor: Ortiz Sánchez, Oswaldo
• Formato: artículo
• Fecha de Publicación: 2012
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revistas - Universidad Nacional Mayor de San Marcos
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/2287
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/iigeo/article/view/2287
• Nivel de acceso: acceso abierto
• Materia: Variable dependiente e independiente; fragmentación; predecir; rendimiento; regresión.; Dependent variable and independent; fragmentation; predict; yield; regression.

The productivity of a rock charger operates on a front surface mining is influenced by many variables. We identified those considered most important and for a type charger model number measurements were taken in the field. Using linear regression expressions that allow predicting the dependent variable as a function of the independent variables were deducted. The dependent variables were identified : production tonnage , fill factor , and delay characteristics of the excavation. The independent variables were : bench height , boot size and fragmentation. Least squares system is used to predict the performance and cost given certain values ​​of the remaining variables in the regression equation but there is the problem of selecting the dependent variable. Two or more variables in the system might seem equally dependent , since the process to test significance of the variable can be iffy .